APOGEE: adaptive prefetching on GPUs for energy efficiency

Ankit Sethia, Ganesh Dasika, Mehrzad Samadi, Scott Mahlke
Advanced Computer Architecture Laboratory, University of Michigan – Ann Arbor, MI
22nd international conference on Parallel architectures and compilation techniques (PACT ’13), 2013
@inproceedings{sethia2013apogee,

   title={APOGEE: adaptive prefetching on GPUs for energy efficiency},

   author={Sethia, Ankit and Dasika, Ganesh and Samadi, Mehrzad and Mahlke, Scott},

   booktitle={Proceedings of the 22nd international conference on Parallel architectures and compilation techniques},

   pages={73–82},

   year={2013},

   organization={IEEE Press}

}

Download Download (PDF)   View View   Source Source   
Modern graphics processing units (GPUs) combine large amounts of parallel hardware with fast context switching among thousands of active threads to achieve high performance. However, such designs do not translate well to mobile environments where power constraints often limit the amount of hardware. In this work, we investigate the use of prefetching as a means to increase the energy efficiency of GPUs. Classically, CPU prefetching results in higher performance but worse energy efficiency due to unnecessary data being brought on chip. Our approach, called APOGEE, uses an adaptive mechanism to dynamically detect and adapt to the memory access patterns found in both graphics and scientific applications that are run on modern GPUs to achieve prefetching efficiencies of over 90%. Rather than examining threads in isolation, APOGEE uses adjacent threads to more efficiently identify address patterns and dynamically adapt the timeliness of prefetching. The net effect of APOGEE is that fewer thread contexts are necessary to hide memory latency and thus sustain performance. This reduction in thread contexts and related hardware translates to simplification of hardware and leads to a reduction in power. For Graphics and GPGPU applications, APOGEE enables an 8X reduction in multithreading hardware, while providing a performance benefit of 19%. This translates to a 52% increase in performance per watt over systems with high multi-threading and 33% over existing GPU prefetching techniques.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

You must be logged in to post a comment.

* * *

* * *

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 11.4
  • SDK: AMD APP SDK 2.8
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 5.0.35, AMD APP SDK 2.8

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us:

contact@hgpu.org